Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early dia...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Iyden Kamil Mohammed, Ali Hussein Al-Timemy, Javier Escudero
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2020
Materias:	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/073a2a1bb523471ebbd51fd2e8940479
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:073a2a1bb523471ebbd51fd2e8940479
record_format	dspace
spelling	oai:doaj.org-article:073a2a1bb523471ebbd51fd2e89404792021-12-02T13:12:40ZTwo-Stage Classification of Breast Tumor Biomarkers for Iraqi Women1818-11712312-0789https://doaj.org/article/073a2a1bb523471ebbd51fd2e89404792020-09-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/678https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women. Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are further classified into either malignant or benign. The collected 20 breast cancer features are utilized to test the performance of the proposed classification system with Leave-One-Out (LOO) cross validation and Synthetic Minority Over-Sampling Technique (SMOTE) to balance the classes. Furthermore, correlation-based feature selection (CFS) was employed in an exploratory analysis to find the best features for the 2-stage classification system. Results: Classification accuracy of 94% for stage-1 and 100% for stage-2was achieved with a Naïve Bayesclassifier which outperformed other three methods. In addition, CFS selected small subset of features as being the best five features out of the all 20 features for both stage-1 and stage-2. Conclusion: We achieved a high classification accuracy which is promising to help improve the early diagnosis of breast tumor. The outcome of this study also shows the importance of CA15-3protein in saliva and blood as well as carcinoembryonic antigen level and total protein in blood, and Estrogen hormone level in saliva, for predicting breast tumors. Iyden Kamil MohammedAli Hussein Al-TimemyJavier EscuderoAl-Khwarizmi College of Engineering – University of BaghdadarticleChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 16, Iss 3 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Iyden Kamil Mohammed Ali Hussein Al-Timemy Javier Escudero Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
description	Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women. Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are further classified into either malignant or benign. The collected 20 breast cancer features are utilized to test the performance of the proposed classification system with Leave-One-Out (LOO) cross validation and Synthetic Minority Over-Sampling Technique (SMOTE) to balance the classes. Furthermore, correlation-based feature selection (CFS) was employed in an exploratory analysis to find the best features for the 2-stage classification system. Results: Classification accuracy of 94% for stage-1 and 100% for stage-2was achieved with a Naïve Bayesclassifier which outperformed other three methods. In addition, CFS selected small subset of features as being the best five features out of the all 20 features for both stage-1 and stage-2. Conclusion: We achieved a high classification accuracy which is promising to help improve the early diagnosis of breast tumor. The outcome of this study also shows the importance of CA15-3protein in saliva and blood as well as carcinoembryonic antigen level and total protein in blood, and Estrogen hormone level in saliva, for predicting breast tumors.
format	article
author	Iyden Kamil Mohammed Ali Hussein Al-Timemy Javier Escudero
author_facet	Iyden Kamil Mohammed Ali Hussein Al-Timemy Javier Escudero
author_sort	Iyden Kamil Mohammed
title	Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
title_short	Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
title_full	Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
title_fullStr	Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
title_full_unstemmed	Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
title_sort	two-stage classification of breast tumor biomarkers for iraqi women
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2020
url	https://doaj.org/article/073a2a1bb523471ebbd51fd2e8940479
work_keys_str_mv	AT iydenkamilmohammed twostageclassificationofbreasttumorbiomarkersforiraqiwomen AT alihusseinaltimemy twostageclassificationofbreasttumorbiomarkersforiraqiwomen AT javierescudero twostageclassificationofbreasttumorbiomarkersforiraqiwomen
_version_	1718393370356744192

Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women

Ejemplares similares